Fast and general method to predict the physicochemical properties of druglike molecules using the integral equation theory of molecular liquids

Research output: Research - peer-reviewArticle

We report a method to predict physico-chemical properties of druglike molecules using a classical statistical mechanics based solvent model combined with machine learning. The RISM-MOL-INF method introduced here provides an accurate technique to characterize solvation and desolvation processes based on solute-solvent correlation functions computed by the 1D Reference Interaction Site Model of the Integral Equation Theory of Molecular Liquids. These functions can be obtained in a matter of minutes for most small organic and druglike molecules using existing software (RISM-MOL) (Sergiievskyi, V. P.; Hackbusch, W.; Fedorov, M. V. J. Comput. Chem. 2011, 32, 1982-1992.). Predictions of caco-2 cell permeability and hydration free energy obtained using the RISM-MOL-INF method are shown to be more accurate than the state-of-the-art tools for benchmark datasets. Due to the importance of solvation and desolvation effects in biological systems, it is anticipated that the RISM-MOL-INF approach will find many applications in biophysical and biomedical property prediction.
Original languageEnglish
Pages (from-to)3420–3432
Number of pages13
JournalMolecular Pharmaceutics
Issue number9
Early online date26 Jul 2015
StatePublished - 8 Sep 2015

    Research areas

  • ADME, ADMET, bioavailability, caco-2, drug discovery, druglike, hydration free energy, IET, integral equation theory of molecular liquids, permeability, QSAR, QSPR, Random Forest, reference interaction site model, RISM, solvation free energy, statistical mechanics

Bibliographical note

This document is the Accepted Manuscript version of a Published Work that appeared in final form in Molecular Pharmaceutics, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see



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